Quantifying the Hurricane Catastrophe Risk to Offshore Wind Power

The U.S. Department of Energy has estimated that over 50 GW of offshore wind power will be required for the United States to generate 20% of its electricity from wind. Developers are actively planning offshore wind farms along the U.S. Atlantic and Gulf coasts and several leases have been signed for offshore sites. These planned projects are in areas that are sometimes struck by hurricanes. We present a method to estimate the catastrophe risk to offshore wind power using simulated hurricanes. Using this method, we estimate the fraction of offshore wind power simultaneously offline and the cumulative damage in a region. In Texas, the most vulnerable region we studied, 10% of offshore wind power could be offline simultaneously because of hurricane damage with a 100‐year return period and 6% could be destroyed in any 10‐year period. We also estimate the risks to single wind farms in four representative locations; we find the risks are significant but lower than those estimated in previously published results. Much of the hurricane risk to offshore wind turbines can be mitigated by designing turbines for higher maximum wind speeds, ensuring that turbine nacelles can turn quickly to track the wind direction even when grid power is lost, and building in areas with lower risk.     

Comments on Willis,Wells and Picou

Most research relating objective indicators of the quality of environment to subjective indicators of the quality of life has failed to find a relationship between the two. This research, by using community data, establishes an association between the local environment and perceived life quality, and in doing so, provides empirical support for focusing on local as welt as national investigations of life quality.     

Incorporating quality into data envelopment analysis of nursing home performance: A case study

When using data envelopment analysis (DEA) as a benchmarking technique for nursing homes, it is essential to include measures of the quality of care. We survey applications where quality has been incorporated into DEA models and consider the concerns that arise when the results show that quality measures have been effectively ignored. Three modeling techniques are identified that address these concerns. Each of these techniques requires some input from management as to the proper emphasis to be placed on the quality aspect of performance. We report the results of a case study in which we apply these techniques to a DEA model of nursing home performance. We examine in depth not only the resulting efficiency scores, but also the benchmark sets and the weights given to the input and output measures. We find that two of the techniques are effective in insuring that DEA results discriminate between high and low quality performance.     

On the Specification of Propensity Scores,With Applications to the Analysis of Trade Policies

A vector autoregression is fit to recent U.S. data on wheat prices, wheat export sales, wheat export shipments, and exchange rates. Forecast error decompositions and out-of-sample forecasts indicate that exchange rates have little influence on wheat sales and shipments.     

STATUS CHANGE AND PERSISTENCE IN CHICAGO SUBURBS: MULTIPLE PATTERNS OF CAUSATION*

An examination of 1970–1980 census data for suburban Chicago indicates that, within the context of persisting inequities, causal links between community characteristics and status change vary by initial status. Individual change in the relative rankings of initially affluent communities is a direct function of housing age and location; residential development and changes in population size have little or no effect on this process. In contrast, cyclical growth is the primary determinant of status change among places of lesser initial affluence. Within the larger context of centrifugal drift and age-related growth cycles, stronger growth and resulting status improvements occur in accessible, underdeveloped newer places that, due to recent suburbanization, have already attained somewhat higher rankings.     

FIRST AND SECOND MARITAL DISSOLUTION: A DECOMPOSITION EXERCISE FOR WHITES AND BLACKS

Observed distributions of marital dissolutions occurring over time (and thus the divorce rate or other measures of marital dissolution) are dependent on two basic factors: (1) the relative distribution across a population of characteristics influencing the probability of marital dissolution and (2) the relative degree to which each of these characteristics raise or lower the probability of marital dissolution. The purpose of this article is to examine the impact of each of these two factors on the observed distributions of marital dissolution for whites and blacks for both first and second marriages. Using an appropriate statistical methodology, the results indicate a relatively complex pattern of differences between marriage orders and race groups.     

Earnings,relative income,and family formation

The probability of first marriage for men who graduated from Wisconsin high schools in 1957 was analyzed with respect to their Social Security earnings records, Wisconsin income tax reports for parents, and other variables. The findings provide no support for Easterlin's hypothesis that marriage will occur earlier when young men judge their economic prospects favorably with respect to their parents' income. However, young men's earnings and time spent in schooling to increase them were found to be important influences on marriage timing. Additional schooling had little effect net of the time it absorbed.